OpenRaiser/Intern-Atlas huggingface dataset:
Trexplorer Super: Topologically Correct Centerline Tree Tracking of Tubular Objects in CT Volumes
Venue: arXiv cs.CV 2025
Year: 2025

Tubular tree structures, such as blood vessels and airways, are essential in human anatomy and accurately tracking them while preserving their topology is crucial for various downstream tasks. Trexplorer is a recurrent model designed for centerline tracking in 3D medical images but it struggles with predicting duplicate branches and terminating tracking prematurely. To address these issues, we present Trexplorer Super, an enhanced version that notably improves performance through novel advancements. However, evaluating centerline tracking models is challenging due to the lack of public datasets. To enable thorough evaluation, we develop three centerline datasets, one synthetic and two real, each with increasing difficulty. Using these datasets, we conduct a comprehensive evaluation of existing state-of-the-art (SOTA) models and compare them with our approach. Trexplorer Super outperforms previous SOTA models on every dataset. Our results also highlight that strong performance on synthetic data does not necessarily translate to real datasets. The code and datasets are available at https://github.com/RomStriker/Trexplorer-Super.
Learning from Imperfect Data: Robust Inference of Dynamic Systems using Simulation-based Generative Model
Venue: arXiv cs.LG 2025
Year: 2025

System inference for nonlinear dynamic models, represented by ordinary differential equations (ODEs), remains a significant challenge in many fields, particularly when the data are noisy, sparse, or partially observable. In this paper, we propose a Simulation-based Generative Model for Imperfect Data (SiGMoID) that enables precise and robust inference for dynamic systems. The proposed approach integrates two key methods: (1) physics-informed neural networks with hyper-networks that constructs an ODE solver, and (2) Wasserstein generative adversarial networks that estimates ODE parameters by effectively capturing noisy data distributions. We demonstrate that SiGMoID quantifies data noise, estimates system parameters, and infers unobserved system components. Its effectiveness is validated validated through realistic experimental examples, showcasing its broad applicability in various domains, from scientific research to engineered systems, and enabling the discovery of full system dynamics.
LiLM-RDB-SFC: Lightweight Language Model with Relational Database-Guided DRL for Optimized SFC Provisioning
Venue: arXiv cs.NI 2025
Year: 2025

Effective management of Service Function Chains (SFCs) and optimal Virtual Network Function (VNF) placement are critical challenges in modern Software-Defined Networking (SDN) and Network Function Virtualization (NFV) environments. Although Deep Reinforcement Learning (DRL) is widely adopted for dynamic network decision-making, its inherent dependency on structured data and fixed action rules often limits adaptability and responsiveness, particularly under unpredictable network conditions. This paper introduces LiLM-RDB-SFC, a novel approach combining Lightweight Language Model (LiLM) with Relational Database (RDB) to answer network state queries to guide DRL model for efficient SFC provisioning. Our proposed approach leverages two LiLMs, Bidirectional and Auto-Regressive Transformers (BART) and the Fine-tuned Language Net T5 (FLAN-T5), to interpret network data and support diverse query types related to SFC demands, data center resources, and VNF availability. Results demonstrate that FLAN-T5 outperforms BART with a lower test loss (0.00161 compared to 0.00734), higher accuracy (94.79% compared to 80.2%), and less processing time (2h 2min compared to 2h 38min). Moreover, when compared to the large language model SQLCoder, FLAN-T5 matches the accuracy of SQLCoder while cutting processing time by 96% (SQLCoder: 54 h 43 min; FLAN-T5: 2 h 2 min).
Fast Non-Episodic Adaptive Tuning of Robot Controllers with Online Policy Optimization
Venue: arXiv cs.RO 2025
Year: 2025

We study online algorithms to tune the parameters of a robot controller in a setting where the dynamics, policy class, and optimality objective are all time-varying. The system follows a single trajectory without episodes or state resets, and the time-varying information is not known in advance. Focusing on nonlinear geometric quadrotor controllers as a test case, we propose a practical implementation of a single-trajectory model-based online policy optimization algorithm, M-GAPS,along with reparameterizations of the quadrotor state space and policy class to improve the optimization landscape. In hardware experiments,we compare to model-based and model-free baselines that impose artificial episodes. We show that M-GAPS finds near-optimal parameters more quickly, especially when the episode length is not favorable. We also show that M-GAPS rapidly adapts to heavy unmodeled wind and payload disturbances, and achieves similar strong improvement on a 1:6-scale Ackermann-steered car. Our results demonstrate the hardware practicality of this emerging class of online policy optimization that offers significantly more flexibility than classic adaptive control, while being more stable and data-efficient than model-free reinforcement learning.
OffsetCrust: Variable-Radius Offset Approximation with Power Diagrams
Venue: arXiv cs.GR 2025
Year: 2025

Offset surfaces, defined as the Minkowski sum of a base surface and a rolling ball, play a crucial role in geometry processing, with applications ranging from coverage motion planning to brush modeling. While considerable progress has been made in computing constant-radius offset surfaces, computing variable-radius offset surfaces remains a challenging problem. In this paper, we present OffsetCrust, a novel framework that efficiently addresses the variable-radius offsetting problem by computing a power diagram. Let $R$ denote the radius function defined on the base surface $S$. The power diagram is constructed from contributing sites, consisting of carefully sampled base points on $S$ and their corresponding off-surface points, displaced along $R$-dependent directions. In the constant-radius case only, these displacement directions align exactly with the surface normals of $S$. Moreover, our method mitigates the misalignment issues commonly seen in crust-based approaches through a lightweight fine-tuning procedure. We validate the accuracy and efficiency of OffsetCrust through extensive experiments, and demonstrate its practical utility in applications such as reconstructing original boundary surfaces from medial axis transform (MAT) representations.
Arcturus: A Cloud Overlay Network for Global Accelerator with Enhanced Performance and Stability
Venue: arXiv cs.NI 2025
Year: 2025

Global Accelerator (GA) services play a vital role in ensuring low-latency, high-reliability communication for real-time interactive applications. However, existing GA offerings are tightly bound to specific cloud providers, resulting in high costs, rigid deployment, and limited flexibility, especially for large-scale or budget-sensitive deployments. Arcturus is a cloud-native GA framework that revisits the design of GA systems by leveraging low-cost, heterogeneous cloud resources across multiple providers. Rather than relying on fixed, high-end infrastructure, Arcturus dynamically constructs its acceleration network and balances performance, stability, and resource efficiency. To achieve this, Arcturus introduces a two-plane design: a forwarding plane that builds a proxy network with adaptive control, and a scheduling plane that coordinates load and routing through lightweight, quantitative optimization. Evaluations under millions of RPS show that Arcturus outperforms commercial GA services by up to 1.7X in acceleration performance, reduces cost by 71%, and maintains over 80% resource efficiency--demonstrating efficient use of cloud resources at scale.
Biological Processing Units: Leveraging an Insect Connectome to Pioneer Biofidelic Neural Architectures
Venue: arXiv cs.NE 2025
Year: 2025

The complete connectome of the Drosophila larva brain offers a unique opportunity to investigate whether biologically evolved circuits can support artificial intelligence. We convert this wiring diagram into a Biological Processing Unit (BPU), a fixed recurrent network derived directly from synaptic connectivity. Despite its modest size 3,000 neurons and 65,000 weights between them), the unmodified BPU achieves 98% accuracy on MNIST and 58% on CIFAR-10, surpassing size-matched MLPs. Scaling the BPU via structured connectome expansions further improves CIFAR-10 performance, while modality-specific ablations reveal the uneven contributions of different sensory subsystems. On the ChessBench dataset, a lightweight GNN-BPU model trained on only 10,000 games achieves 60% move accuracy, nearly 10x better than any size transformer. Moreover, CNN-BPU models with ~2M parameters outperform parameter-matched Transformers, and with a depth-6 minimax search at inference, reach 91.7% accuracy, exceeding even a 9M-parameter Transformer baseline. These results demonstrate the potential of biofidelic neural architectures to support complex cognitive tasks and motivate scaling to larger and more intelligent connectomes in future work.
Modeling Understanding of Story-Based Analogies Using Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

Recent advancements in Large Language Models (LLMs) have brought them closer to matching human cognition across a variety of tasks. How well do these models align with human performance in detecting and mapping analogies? Prior research has shown that LLMs can extract similarities from analogy problems but lack robust human-like reasoning. Building on Webb, Holyoak, and Lu (2023), the current study focused on a story-based analogical mapping task and conducted a fine-grained evaluation of LLM reasoning abilities compared to human performance. First, it explored the semantic representation of analogies in LLMs, using sentence embeddings to assess whether they capture the similarity between the source and target texts of an analogy, and the dissimilarity between the source and distractor texts. Second, it investigated the effectiveness of explicitly prompting LLMs to explain analogies. Throughout, we examine whether LLMs exhibit similar performance profiles to those observed in humans by evaluating their reasoning at the level of individual analogies, and not just at the level of overall accuracy (as prior studies have done). Our experiments include evaluating the impact of model size (8B vs. 70B parameters) and performance variation across state-of-the-art model architectures such as GPT-4 and LLaMA3. This work advances our understanding of the analogical reasoning abilities of LLMs and their potential as models of human reasoning.
Mind the Gap: Bridging Occlusion in Gait Recognition via Residual Gap Correction
Venue: arXiv cs.CV 2025
Year: 2025

Gait is becoming popular as a method of person re-identification because of its ability to identify people at a distance. However, most current works in gait recognition do not address the practical problem of occlusions. Among those which do, some require paired tuples of occluded and holistic sequences, which are impractical to collect in the real world. Further, these approaches work on occlusions but fail to retain performance on holistic inputs. To address these challenges, we propose RG-Gait, a method for residual correction for occluded gait recognition with holistic retention. We model the problem as a residual learning task, conceptualizing the occluded gait signature as a residual deviation from the holistic gait representation. Our proposed network adaptively integrates the learned residual, significantly improving performance on occluded gait sequences without compromising the holistic recognition accuracy. We evaluate our approach on the challenging Gait3D, GREW and BRIAR datasets and show that learning the residual can be an effective technique to tackle occluded gait recognition with holistic retention. We release our code publicly at https://github.com/Ayush-00/rg-gait.
A Decision Procedure for Probabilistic Kleene Algebra with Angelic Nondeterminism
Venue: arXiv cs.FL 2025
Year: 2025

We give a decision procedure and proof of correctness for the equational theory of probabilistic Kleene algebra with angelic nondeterminism introduced in Ong, Ma, and Kozen (2025).
An Exploratory Study on AI-driven Visualisation Techniques on Decision Making in Extended Reality
Venue: arXiv cs.HC 2025
Year: 2025

The integration of extended reality (XR) with artificial intelligence (AI) introduces a new paradigm for user interaction, enabling AI to perceive user intent, stimulate the senses, and influence decision-making. We explored the impact of four AI-driven visualisation techniques -- `Inform,' `Nudge,' `Recommend,' and `Instruct' -- on user decision-making in XR using the Meta Quest Pro. To test these techniques, we used a pre-recorded 360-degree video of a supermarket, overlaying each technique through a virtual interface. We aimed to investigate how these different visualisation techniques with different levels of user autonomy impact preferences and decision-making. An exploratory study with semi-structured interviews provided feedback and design recommendations. Our findings emphasise the importance of maintaining user autonomy, enhancing AI transparency to build trust, and considering context in visualisation design.
AdaMuon: Adaptive Muon Optimizer
Venue: arXiv cs.LG 2025
Year: 2025

We propose AdaMuon, a novel optimizer that combines element-wise adaptivity with orthogonal updates for large-scale neural network training. AdaMuon incorporates two tightly coupled mechanisms: (1) an element-wise second momentum estimator applied to orthogonalized update directions, and (2) a sign-stabilized orthogonal update, where the momentum is first sign-transformed before orthogonalization. These two components jointly enable variance-adaptive scaling while maintaining stable update geometry. In addition, AdaMuon employs an RMS-aligned rescaling strategy to match the root-mean-square update magnitude to Adam, allowing direct reuse of existing learning rate schedules without extra tuning. Experiments demonstrate that AdaMuon not only maintains stability but can surpass Adam by more than 40\% training efficiency in large-scale scenarios.
Asymptotically Optimal Repair of Reed-Solomon Codes with Small Sub-Packetization under Rack-Aware Model
Venue: arXiv cs.IT 2025
Year: 2025

This paper presents a comprehensive study on the asymptotically optimal repair of Reed-Solomon (RS) codes with small sub-packetization, specifically tailored for rack-aware distributed storage systems. Through the utilization of multi-base expansion, we introduce a novel approach that leverages monomials to construct linear repair schemes for RS codes. Our repair schemes which adapt to all admissible parameters achieve asymptotically optimal repair bandwidth while significantly reducing the sub-packetization compared with existing schemes. Furthermore, our approach is capable of repairing RS codes with asymptotically optimal repair bandwidth under the homogeneous storage model, achieving smaller sub-packetization than existing methods.
LogTinyLLM: Tiny Large Language Models Based Contextual Log Anomaly Detection
Venue: arXiv cs.LG 2025
Year: 2025

Log anomaly detection using traditional rule based or deep learning based methods is often challenging due to the large volume and highly complex nature of log sequence. So effective way of detection of anomalous sequence of logs is crucial for system maintenance and development. This paper proposes parameter efficient finetuning specifically low rank adaptation (LoRA) and adapter based approaches for finding contextual anomalies in sequence of logs in large log data set. It compares different tiny large language models (LLMs) on the Thunderbird dataset. The results show that LoRA based finetuning provides substantial performance improvements of 18 to 19 percentage over LogBert based full finetuning approach, achieving accuracy scores between 97.76% and 98.83% compared to 79.37%.
Joint angle model based learning to refine kinematic human pose estimation
Venue: arXiv cs.CV 2025
Year: 2025

Marker-free human pose estimation (HPE) has found increasing applications in various fields. Current HPE suffers from occasional errors in keypoint recognition and random fluctuation in keypoint trajectories when analyzing kinematic human poses. The performance of existing deep learning-based models for HPE refinement is considerably limited by inaccurate training datasets in which the keypoints are manually annotated. This paper proposed a novel method to overcome the difficulty through joint angle-based modeling. The key techniques include: (i) A joint angle-based model of human pose, which is robust to describe kinematic human poses; (ii) Approximating temporal variation of joint angles through high order Fourier series to get reliable "ground truth"; (iii) A bidirectional recurrent network is designed as a post-processing module to refine the estimation of well-established HRNet. Trained with the high-quality dataset constructed using our method, the network demonstrates outstanding performance to correct wrongly recognized joints and smooth their spatiotemporal trajectories. Tests show that joint angle-based refinement (JAR) outperforms the state-of-the-art HPE refinement network in challenging cases like figure skating and breaking.
Execution and monitoring of HOA automata with HOAX
Venue: arXiv cs.LO 2025
Year: 2025

We present a tool called Hoax for the execution of ω-automata expressed in the popular HOA format. The tool leverages the notion of trap sets to enable runtime monitoring of any (non-parity) acceptance condition supported by the format. When the automaton is not monitorable, the tool may still be able to recognise so-called ugly prefixes, and determine that no further observation will ever lead to a conclusive verdict. The tool is open-source and highly configurable. We present its formal foundations, its design, and compare it against the trace analyser PyContract on a lock acquisition scenario.
Hashed Watermark as a Filter: Defeating Forging and Overwriting Attacks in Weight-based Neural Network Watermarking
Venue: arXiv cs.CR 2025
Year: 2025

As valuable digital assets, deep neural networks necessitate robust ownership protection, positioning neural network watermarking (NNW) as a promising solution. Among various NNW approaches, weight-based methods are favored for their simplicity and practicality; however, they remain vulnerable to forging and overwriting attacks. To address those challenges, we propose NeuralMark, a robust method built around a hashed watermark filter. Specifically, we utilize a hash function to generate an irreversible binary watermark from a secret key, which is then used as a filter to select the model parameters for embedding. This design cleverly intertwines the embedding parameters with the hashed watermark, providing a robust defense against both forging and overwriting attacks. An average pooling is also incorporated to resist fine-tuning and pruning attacks. Furthermore, it can be seamlessly integrated into various neural network architectures, ensuring broad applicability. Theoretically, we analyze its security boundary. Empirically, we verify its effectiveness and robustness across 13 distinct Convolutional and Transformer architectures, covering five image classification tasks and one text generation task. The source codes are available at https://github.com/AIResearch-Group/NeuralMark.
Automata Models for Effective Bug Pattern Description
Venue: arXiv cs.SE 2025
Year: 2025

Debugging complex systems is a crucial yet time-consuming task. This paper presents the use of automata learning and testing techniques to obtain concise and informative bug descriptions. We introduce the concepts of Failure Explanations (FE), Eventual Failure Explanations (EFE), and Early Detection (ED) to provide meaningful summaries of failing behavior patterns. By factoring out irrelevant information and focusing on essential test patterns, our approach aims to enhance bug detection and understanding. We evaluate our methods using various test patterns and real-world benchmarks, demonstrating their effectiveness in producing compact and informative bug descriptions.
LISA -- A Modern Proof System
Venue: arXiv cs.LO 2025
Year: 2025

We present LISA, a proof system and proof assistant for constructing proofs in schematic first-order logic and axiomatic set theory. The logical kernel of the system is a proof checker for first-order logic with equality and schematic predicate and function symbols. It implements polynomial-time proof checking and uses the axioms of ortholattices (which implies the irrelevance of the order of conjuncts and disjuncts and additional propositional laws). The kernel supports the notion of theorems (whose proofs are not expanded), as well as definitions of predicate symbols and objects whose unique existence is proven. A domain-specific language enables construction of proofs and development of proof tactics with user-friendly tools and presentation, while remaining within the general-purpose language, Scala. We describe the LISA proof system and illustrate the flavour and the level of abstraction of proofs written in LISA. This includes a proof-generating tactic for propositional tautologies, leveraging the ortholattice properties to reduce the size of proofs. We also present early formalization of set theory in LISA, including Cantor's theorem.
An Explainable AI-Enhanced Machine Learning Approach for Cardiovascular Disease Detection and Risk Assessment
Venue: arXiv cs.LG 2025
Year: 2025

Heart disease remains a major global health concern, particularly in regions with limited access to medical resources and diagnostic facilities. Traditional diagnostic methods often fail to accurately identify and manage heart disease risks, leading to adverse outcomes. Machine learning has the potential to significantly enhance the accuracy, efficiency, and speed of heart disease diagnosis. In this study, we proposed a comprehensive framework that combines classification models for heart disease detection and regression models for risk prediction. We employed the Heart Disease dataset, which comprises 1,035 cases. To address the issue of class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied, resulting in the generation of an additional 100,000 synthetic data points. Performance metrics, including accuracy, precision, recall, F1-score, R2, MSE, RMSE, and MAE, were used to evaluate the model's effectiveness. Among the classification models, Random Forest emerged as the standout performer, achieving an accuracy of 97.2% on real data and 97.6% on synthetic data. For regression tasks, Linear Regression demonstrated the highest R2 values of 0.992 and 0.984 on real and synthetic datasets, respectively, with the lowest error metrics. Additionally, Explainable AI techniques were employed to enhance the interpretability of the models. This study highlights the potential of machine learning to revolutionize heart disease diagnosis and risk prediction, thereby facilitating early intervention and enhancing clinical decision-making.